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Found 18 papers, 10 papers with code
A Review of Barren Plateaus in Variational Quantum Computing
Variational quantum computing offers a flexible computational paradigm with applications in diverse areas.
Learning shallow quantum circuits
Despite fundamental interests in learning quantum circuits, the existence of a computationally efficient algorithm for learning shallow quantum circuits remains an open question.
Exponential Quantum Communication Advantage in Distributed Inference and Learning
To our knowledge, this is the first example of exponential quantum advantage for a generic class of machine learning problems that hold regardless of the data encoding cost.
Revisiting dequantization and quantum advantage in learning tasks
In this note, we prove that classical algorithms with SQ access can accomplish some learning tasks exponentially faster than quantum algorithms with quantum state inputs.
Quantum advantage in learning from experiments
Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world.
Exponential suppression of bit or phase flip errors with repetitive error correction
QEC also requires that the errors are local and that performance is maintained over many rounds of error correction, two major outstanding experimental challenges.
Quantum Physics
Variational Quantum Algorithms
Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost.
Even more efficient quantum computations of chemistry through tensor hypercontraction
We describe quantum circuits with only $\widetilde{\cal O}(N)$ Toffoli complexity that block encode the spectra of quantum chemistry Hamiltonians in a basis of $N$ arbitrary (e. g., molecular) orbitals.
Quantum Physics Chemical Physics
Power of data in quantum machine learning
These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems.
Observation of separated dynamics of charge and spin in the Fermi-Hubbard model
Strongly correlated quantum systems give rise to many exotic physical phenomena, including high-temperature superconductivity.
Quantum Physics
Layerwise learning for quantum neural networks
In order to ameliorate some of these challenges, we investigate a layerwise learning strategy for parametrized quantum circuits.
Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
For problems defined on our hardware graph we obtain an approximation ratio that is independent of problem size and observe, for the first time, that performance increases with circuit depth.
Quantum Physics
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
Learning to learn with quantum neural networks via classical neural networks
Quantum Neural Networks (QNNs) are a promising variational learning paradigm with applications to near-term quantum processors, however they still face some significant challenges.
Efficient population transfer via non-ergodic extended states in quantum spin glass
NEE provide a mechanism for population transfer (PT) between computational states and therefore can serve as a new quantum subroutine for quantum search, quantum parallel tempering and reverse annealing optimization algorithms.
Quantum Physics Disordered Systems and Neural Networks Statistical Mechanics Strongly Correlated Electrons
Barren plateaus in quantum neural network training landscapes
Specifically, we show that for a wide class of reasonable parameterized quantum circuits, the probability that the gradient along any reasonable direction is non-zero to some fixed precision is exponentially small as a function of the number of qubits.
OpenFermion: The Electronic Structure Package for Quantum Computers
Quantum simulation of chemistry and materials is predicted to be an important application for both near-term and fault-tolerant quantum devices.
Quantum Physics Chemical Physics Computational Physics
Exploiting locality in quantum computation for quantum chemistry
Accurate prediction of chemical and material properties from first principles quantum chemistry is a challenging task on traditional computers.
Quantum Physics Chemical Physics
